A basic understanding of how trans-acting factors, such as microRNAs, organize different coding RNAs into the same regulatory pathway to manage gene expression is essential to begin understanding the origins of translation dysfunction, a key contributor to disease. A major goal is to be able to predict in vivo expression patterns from primary cis-regulatory RNA sequence, as is common for DNA sequences. A linear relationship model, where all RNAs containing the same motif belong to the same regulatory pathway, is insufficient to explain in vivo patterns. For example, the translation of co-expressed RNAs that contain equivalent microRNA binding motifs is often regulated differently. A recent ex vivo study using frog extracts examined three known motifs on five differentially expressed RNAs, and found that single regulators and single motifs are the building blocks of a nonlinear 'code'system, where functionally distinct co-factor complexes engage different combinations of motifs. However, the arrangement of three motifs is sufficient to explain only a fraction of RNA regulatory patterns during a complex biological event. To build a broad physiological model that will allow me to analyze and predict the collective behavior of many RNAs during a complex biological event, I will assemble a high-resolution 'germ code'by characterizing the relationship between sequence and regulation for 140 RNAs during germ cell development in the Drosophila melanogaster embryo. I will identify trans-acting proteins governing each group by a candidate gene approach screen looking for regulatory pattern changes to a few representative RNAs, and I will identify trans-acting small RNAs by 'deep sequencing'isolated embryonic germ cells. I will use a bioinformatics approach, perhaps supplemented by a phylogenetic component, to determine RNA primary sequences and secondary structures statistically enriched in the RNAs that populate each regulatory pathway, and depleted in all others. Not all germ cell RNAs are known, so I will test my 'germ code'by scanning the fly genome for novel germlinne RNAs. I will identify candidates, predict the pathway(s) they belong to, and experimentally validate my predictions. This work will lead to novel insights into the regulatory logic of RNA during development, and will set the stage for investigating how disruptions in this process fit into the pathology of disease.